Electronic apparatus and control method thereof

ABSTRACT

An electronic apparatus and a control method thereof are provided. A method of controlling an electronic apparatus according to an embodiment of the disclosure includes: receiving input of a first utterance, identifying a first task for the first utterance based on the first utterance, providing a response to the first task based on a predetermined response pattern, receiving input of a second utterance, identifying a second task for the second utterance based on the second utterance, determining the degree of association between the first task and the second task, and setting a response pattern for the first task based on the second task based on the determined degree of association satisfying a predetermined condition. The control method of an electronic apparatus may use an artificial intelligence model trained according to at least one of machine learning, a neural network, or a deep learning algorithm.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on and claims priority under 35 U.S.C. § 119to Korean Patent Application No. 10-2018-0132717, filed on Nov. 1, 2018,in the Korean Intellectual Property Office, and Korean PatentApplication No. 10-2019-0129837, filed on Oct. 18, 2019 in the KoreanIntellectual Property Office, the disclosures of which are incorporatedby reference herein in their entireties.

BACKGROUND 1. Field

The disclosure relates to an electronic apparatus and a control methodthereof, and for example, to an electronic apparatus capable ofproviding a response to a user utterance and an additional responserelated to the user utterance, and a control method thereof.

The disclosure also relates to an artificial intelligence (AI) systemsimulating functions of a human brain such as cognition anddetermination utilizing a machine learning algorithm, and applicationthereof.

2. Description of Related Art

An artificial intelligence (AI) system may refer to a computer systemimplementing intelligence of a human level, and may include a systemwherein a machine learns, determines, and becomes smarter by itself,unlike conventional rule-based smart systems. An artificial intelligencesystem shows a more improved recognition rate as it is used more, andbecomes capable of understanding user preference more correctly. Forthis reason, conventional rule-based smart systems are gradually beingreplaced by deep learning-based artificial intelligence systems.

An artificial intelligence technology may include machine learning (deeplearning) and element technologies utilizing machine learning.

Machine learning may refer, for example, to an algorithm technology ofclassifying/learning the characteristics of input data by itself.Meanwhile, an element technology may refer, for example, to a technologyutilizing a machine learning algorithm such as deep learning, andincludes fields of technologies such as linguistic understanding, visualunderstanding, inference/prediction, knowledge representation, andoperation control.

Examples of various fields to which artificial intelligence technologiesare applied are as follows. Linguistic understanding may refer, forexample, to a technology of recognizing languages/characters of humans,and applying/processing them, and includes natural speech processing,machine translation, dialog systems, queries and answers, voicerecognition/synthesis, and the like. Visual understanding may refer, forexample, to a technology of recognizing an object in a similar manner tohuman vision, and processing the object, and may include recognition ofan object, tracking of an object, search of an image, recognition ofhumans, understanding of a scene, understanding of a space, improvementof an image, and the like. Inference/prediction may refer, for example,to a technology of determining information and making logical inferenceand prediction, and may include knowledge/probability based inference,optimization prediction, preference based planning, recommendation, andthe like. Knowledge representation may refer, for example, to atechnology of automatically processing information of human experiencesinto knowledge data, and may include knowledge construction (datageneration/classification), knowledge management (data utilization), andthe like. Operation control may refer, for example, to a technology ofcontrolling autonomous driving of vehicles and movements of robots, andmay include movement control (navigation, collision, driving), operationcontrol (behavior control), and the like.

Meanwhile, in the case of an electronic apparatus including a dialogsystem providing a response to a user inquiry, the electronic apparatusmerely provides a response to a user inquiry, but does not provide anadditional response to the user inquiry. Accordingly, there isinconvenience that a user has to perform a lot of inquiries or a longinquiry for achieving desired information, and also, there is a problemthat a dialog time between a user and a dialog system becomes long.

SUMMARY

Embodiments of the disclosure address the aforementioned problem, andrelate to an electronic apparatus capable of providing a response to auser utterance and an additional response related to the user utterancebased on the preference information and dialog history of the user, anda control method thereof.

An example control method of an electronic apparatus according to anexample embodiment includes the steps of receiving input of a firstutterance, identifying a first task for the first utterance based on thefirst utterance, providing a response to the first task based on apredetermined response pattern, receiving input of a second utterance,identifying a second task for the second utterance based on the secondutterance, and determining a degree of association between the firsttask and the second task, and setting a response pattern for the firsttask based on the second task based on the determined degree ofassociation satisfying a predetermined condition.

The response pattern may be determined while including at least one ofinformation related to the length of a response to an utterance orinformation related to the types of information included in the responseto the utterance.

The predetermined response pattern may be a response pattern selected bya command, or a response pattern automatically set based on theutterance history of the electronic apparatus.

The method may input a voice according to the first utterance into atrained artificial intelligence model to obtain information on anacoustic feature of the first utterance, and recognize the user based onthe information on the obtained acoustic feature. The predeterminedresponse pattern may be determined based on the recognized conversationhistory and preference information of the user.

The predetermined condition may be a condition wherein information onthe degree of association between the first task and the second task isequal to or greater than a threshold value.

The control method may further include the steps of determining whethera third task for a third utterance is associated with the first taskbased on receiving input of a third utterance, and providing a responsebased on the set response pattern for the first task.

The control method may further include the steps of receiving input of athird utterance, and identifying a third task for the third utterancebased on the third utterance, and determining the degree of associationbetween the first task and the third task. The step of setting mayfurther include the steps of determining priorities of the second taskand the third task, based on the determined degree of associationbetween the first task and the third task satisfying a predeterminedcondition, and setting a response pattern for the first task based onthe determined priorities, the second task, and the third task.

The control method may include the steps of acquiring feedback for theprovided response, and updating the degree of association between thefirst task and the second task based on the acquired feedback.

The control method may further include the step of storing the firsttask and the second task in the form of ontology based on the degree ofassociation between the first task and the second task.

At least one of the steps of identifying a first task, determining thedegree of association, setting a response pattern, or providing aresponse may be performed by an artificial intelligence model. Theartificial intelligence model may include a plurality of layersrespectively including at least one node, and each of the at least onenode may include a neural network model having a connection weight forinterpretation of an input value.

An example electronic apparatus according to an example embodiment ofthe disclosure may include a memory configured to store at least onecommand, and a processor configured to execute the at least one commandto control the electronic apparatus. The processor may control theelectronic apparatus to: receive input of a first utterance, identify afirst task for the first utterance based on the first utterance, providea response to the first task based on a predetermined response pattern,and receive input of a second utterance, identify a second task for thesecond utterance based on the second utterance, and determine a degreeof association between the first task and the second task, and set aresponse pattern for the first task based on the second task based onthe determined degree of association satisfying a predeterminedcondition.

The response pattern may be determined while including at least one ofinformation related to the length of a response to an utterance orinformation related to the types of information included in the responseto the utterance.

The predetermined response pattern may be a response pattern selected bya command, or a response pattern automatically set based on theutterance history of the electronic apparatus.

The predetermined condition may be a condition wherein information onthe degree of association between the first task and the second task isequal to or greater than a threshold value.

The processor may control the electronic apparatus to input a voiceaccording to the first utterance into a trained artificial intelligencemodel to obtain information on an acoustic feature of the firstutterance, and recognize the user based on the information on theobtained acoustic feature. The predetermined response pattern isdetermined based on the recognized conversation history and preferenceinformation of the user.

The processor may control the electronic apparatus to determine whethera third task for a third utterance is associated with the first taskbased on receiving input of a third utterance, and provide a responsebased on the set response pattern for the first task.

The processor may control the electronic apparatus to receive input of athird utterance, identify a third task for the third utterance based onthe third utterance, and determine the degree of association between thefirst task and the third task, and determine priorities of the secondtask and the third task based on the determined degree of associationbetween the first task and the third task satisfying a predeterminedcondition, and set a response pattern for the first task based on thedetermined priorities, the second task, and the third task.

In addition, the processor may control the electronic apparatus toacquire a feedback for the provided response, and update the degree ofassociation between the first task and the second task based on theacquired feedback.

The processor may control the electronic apparatus to store the firsttask and the second task in the form of ontology in the memory based onthe degree of association between the first task and the second task.

At least one of the operations of identifying a first task, determiningthe degree of association, setting a response pattern, or providing aresponse may be performed by an artificial intelligence model. Theartificial intelligence model may include a plurality of layersrespectively including at least one node, and each of the at least onenode may include a neural network model having a connection weight forinterpretation of an input value.

According to the aforementioned various example embodiments of thedisclosure, an electronic apparatus may provide various responses to auser utterance, and thereby reduce unnecessary dialog turns.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features and advantages of certainembodiments of the present disclosure will be more apparent from thefollowing detailed description, taken in conjunction with theaccompanying drawings, in which:

FIG. 1 is a diagram illustrating an example operation of an exampleelectronic apparatus according to an embodiment of the disclosure;

FIG. 2 is a block diagram illustrating an example configuration of anexample electronic apparatus according to an embodiment of thedisclosure;

FIG. 3 is a block diagram illustrating an example configuration of anexample electronic apparatus according to an embodiment of thedisclosure;

FIG. 4 is a block diagram illustrating an example dialog systemaccording to an embodiment of the disclosure;

FIG. 5A is a flowchart illustrating an example method for providing aresponse to a user utterance according to an embodiment of thedisclosure;

FIG. 5B is a diagram illustrating an example method for providing anadditional response according to another embodiment of the disclosure;

FIG. 6A is a diagram illustrating an example method for updatinginformation on the degree of association among tasks according to anembodiment of the disclosure;

FIG. 6B is a diagram illustrating an example regarding the informationon the degree of association among tasks stored in the knowledgedatabase 460 according to an embodiment of the disclosure.

FIG. 7 is a diagram illustrating an example of providing a response to auser utterance according to an embodiment of the disclosure;

FIG. 8A is a diagram illustrating an example in which a response to auser utterance is an inquiry type according to another embodiment of thedisclosure;

FIG. 8B is a diagram illustrating an example in which a response to auser utterance is an inquiry type according to another embodiment of thedisclosure;

FIG. 9 is a flowchart illustrating an example method for providing aninquiry type response to a user utterance according to an embodiment ofthe disclosure;

FIG. 10 is a diagram illustrating an example of providing a response toa user utterance according to still another embodiment the disclosure;

FIG. 11 is a diagram illustrating an example system including anelectronic apparatus and a server according to an embodiment of thedisclosure;

FIG. 12 is a flowchart illustrating an example method of controlling anelectronic apparatus according to an embodiment of the disclosure; and

FIG. 13 is a flowchart illustrating an example method of controlling anelectronic apparatus according to another embodiment of the disclosure.

DETAILED DESCRIPTION

Hereinafter, various example embodiments of the disclosure will bedescribed with reference to the accompanying drawings. Meanwhile, itshould be noted that the various example embodiments are not forlimiting the technology described in the disclosure to a specificembodiment, but they should be interpreted to include variousmodifications, equivalents and/or alternatives of the embodiments of thedisclosure. Also, with respect to the descriptions of drawings, similarreference numerals may be used for similar components.

In the disclosure, expressions such as “have,” “may have,” “include” and“may include” should be understood as denoting that there are suchcharacteristics (e.g.: elements such as numerical values, functions,operations and components), and the expressions are not intended toexclude the existence of additional characteristics.

In the disclosure, the expressions “A or B,” “at least one of A and/orB,” or “one or more of A and/or B” and the like may include all possiblecombinations of the listed items. For example, “A or B,” “at least oneof A and B,” or “at least one of A or B” refer to all of the followingcases: (1) including at least one A, (2) including at least one B, or(3) including at least one A and at least one B.

The expressions “first,” “second” and the like used in the disclosuremay be used to describe various elements regardless of any order and/ordegree of importance. Such expressions may be used to distinguish oneelement from another element, and are not intended to limit theelements.

The description in the disclosure that one element (e.g.: a firstelement) is “(operatively or communicatively) coupled with/to” or“connected to” another element (e.g.: a second element) should beunderstood to include both the case where the one element is directlycoupled to the another element, and the case where the one element iscoupled to the another element through still another element (e.g.: athird element). The description that one element (e.g.: a first element)is “directly coupled” or “directly connected” to another element (e.g.:a second element) may be interpreted to refer to a situation in whichstill another element (e.g.: a third element) does not exist between theone element and the another element.

The expression “configured to” used in the disclosure may beinterchangeably used with other expressions such as “suitable for,”“having the capacity to,” “designed to,” “adapted to,” “made to” and“capable of,” depending on cases. The term “configured to” does notnecessarily refer to a device that is “specifically designed to” interms of hardware. Instead, under some circumstances, the expression “adevice configured to” may refer to a device that “is capable of”performing an operation together with another device or component. Forexample, the phrase “a sub-processor configured to perform A, B and C”may refer, for example, and without limitation, to a dedicated processor(e.g.: an embedded processor) for performing the correspondingoperations, a generic-purpose processor (e.g.: a CPU or an applicationprocessor) that can perform the corresponding operations by executingone or more software programs stored in a memory device, or the like.

An electronic apparatus according to various example embodiments of thedisclosure may include, for example, and without limitation, at leastone of a smartphone, a tablet PC, a mobile phone, a video phone, ane-book reader, a desktop PC, a laptop PC, a netbook computer, aworkstation, a server, a PDA, a portable multimedia player (PMP), an MP3player, a medical instrument, a camera, a wearable device, or the like.A wearable device may include, for example, and without limitation, atleast one of an accessory-type device (e.g.: a watch, a ring, abracelet, an ankle bracelet, a necklace, glasses, a contact lens, or ahead-mounted-device (HMD)), a device integrated with fabrics or clothing(e.g.: electronic clothing), a body-attached device (e.g.: a skin pad ora tattoo), an implantable circuit, or the like. In some embodiments, anelectronic apparatus may include, for example, and without limitation,at least one of a television, a digital video disk (DVD) player, anaudio, a refrigerator, an air conditioner, a cleaner, an oven, amicrowave oven, a washing machine, an air cleaner, a set top box, a homeautomation control panel, a security control panel, a media box (e.g.:Samsung HomeSync™, Apple TV™, or Google TV™), a game console (e.g.:Xbox™, PlayStation™), an electronic dictionary, an electronic key, acamcorder, an electronic photo frame, or the like.

In another example embodiment of the disclosure, an electronic apparatusmay include, for example, and without limitation, at least one ofvarious types of medical instruments (e.g.: various types of portablemedical measurement instruments (a blood glucose meter, a heart ratemeter, a blood pressure meter, or a thermometer, etc.), magneticresonance angiography (MRA), magnetic resonance imaging (MRI), computedtomography (CT), a photographing device, or an ultrasonic instrument,etc.), a navigation device, a global navigation satellite system (GNSS),an event data recorder (EDR), a flight data recorder (1-DR), a vehicleinfotainment device, an electronic device for vessels (e.g.: anavigation device for vessels, a gyrocompass, etc.), avionics, asecurity device, a head unit for a vehicle, an industrial or householdrobot, a drone, an ATM of a financial institution, a point of sales(POS) of a store, an Internet of Things (IoT) device (e.g.: a lightbulb, various types of sensors, a sprinkler device, a fire alarm, athermostat, a street light, a toaster, exercise equipment, a hot watertank, a heater, a boiler, etc.), or the like.

In the disclosure, the term “user” may refer to a person who uses anelectronic apparatus or an apparatus using an electronic apparatus(e.g.: an artificial intelligence electronic apparatus).

Hereinafter, the disclosure will be described in greater detail withreference to the accompanying drawings.

FIG. 1 is a diagram illustrating an example operation of an electronicapparatus according to an embodiment of the disclosure.

As illustrated at the left side of FIG. 1, an electronic apparatus 100(refer, for example, to FIG. 2) may include a dialog system forproviding a response to a user inquiry. A conventional dialog systemcould provide only a response to a user inquiry as illustrated in theleft side of FIG. 1. For example, the electronic apparatus 100 mayprovide only a response informing about the temperature to an inquiryinquiring about the temperature, and only a response informing about theweather to an inquiry inquiring about the weather, and only a responseinforming about the degree of atmospheric pollution to an inquiryinquiring about the degree of atmospheric pollution.

According to the conventional dialog system illustrated in the left sideof FIG. 1, a user could perform respective inquiries regarding atemperature check, a weather check, and an atmospheric pollution checkand acquire responses for the respective inquiries, or perform aninquiry inquiring about all of a temperature check, a weather check, andan atmospheric pollution check and acquire a response.

However, even when a user of the electronic apparatus 100 mostlyperforms an inquiry inquiring about the weather and an inquiry inquiringabout the degree of atmospheric pollution together with an inquiryinquiring about the temperature, the electronic apparatus 100 providesonly responses to the user inquiries, and thus unnecessary dialog turnsare generated. Accordingly, according to an embodiment of thedisclosure, for reducing unnecessary dialog turns, the electronicapparatus 100 may learn the dialog history of the user and provideinformation on the temperature and atmospheric pollution regarding aninquiry inquiring about the weather, as illustrated at the right side ofFIG. 1.

For example, the electronic apparatus 100 may provide both a response toa user inquiry and a response to an additional inquiry that a user isexpected to additionally ask, based, for example, on the dialog historyof the user, the preference information set by the user, etc.

According to an embodiment of the disclosure, the electronic apparatus100 may receive input of a first utterance of a user, identify a firsttask for the first utterance, and provide a response to the first taskaccording to a predetermined response pattern. Thereafter, theelectronic apparatus 100 may receive input of a second utterance,identify a second task for the second utterance, and based on the degreeof association between the first task and the second task satisfying apredetermined condition, set a response pattern for the first task basedon the second task. Accordingly, where the first utterance is inputagain at a later time, a response may be provided according to theresponse pattern for the first task set based on the second task.

A response pattern may refer, for example, to at least one of the lengthof a response, the types of information included in a response and/orthe number of pieces of information included in a response. Setting of aresponse pattern may include newly generating a response pattern, andchanging the previous response pattern. Setting of a response patternmay be performed by a manual method by a user, or may be performedautomatically.

According to an embodiment, the electronic apparatus 100 may performobtaining or identifying a user. Accordingly, the electronic apparatus100 may separately store different user conversation history andpreference information set by the user for each user.

For example, when the electronic apparatus 100 is logged in to anAI-based service, such as a smartphone, the electronic apparatus 100 mayupdate, based on a user utterance, the user's conversation history andpreference information which are currently logged in.

When the electronic apparatus 100 is a common apparatus that can be usedby a plurality of users, such as an AI speaker, the electronic apparatus100 may recognize a user based on a user utterance. A process ofrecognizing the user may include, for example, a process of determiningthe user based on features of tone, intonation, pronunciation, speed, orthe like of the user utterance. For example, the electronic apparatus100 may input a received user utterance into a trained artificialintelligence model, and obtain information on an acoustic feature of auser utterance, such as information on a speech of a user utterance,information on unit phonemes of a user utterance, and the like, throughthe trained artificial intelligence model. In addition, the electronicapparatus 100 may compare information on the obtained acoustic featureof the user utterance with the pre-stored information on the acousticfeature of the user utterance, and recognize a user who is a subject ofthe received user utterance.

The electronic apparatus 100 may, based on the user being recognizedbased on the user utterance, update the recognized conversation historyand preference information of the user.

When the electronic apparatus 100 includes a camera, the electronicapparatus may obtain a user image through the camera, and recognize theuser by performing object recognition based on the obtained user image.

For example, the electronic apparatus 100 may extract a feature of anobject included in the obtained user image, obtain a probability that anobject included in the obtained user image corresponds to each of aplurality of categories for classifying an object based on the extractedobject feature, and obtain an object included in the obtained userimage. When the object included in the obtained user image isrecognized, the electronic apparatus 100 may compare the information onthe identified object with the information on the object included in thepre-stored user image, and recognize a user corresponding to the userimage.

A process of recognizing a user based on the user image, like theprocess of recognizing a user based on a user utterance, may, forexample, be performed through an artificial intelligence model includingartificial neural networks, such as, for example, and withoutlimitation, a deep neural network (DNN), convolution neural network(CNN), recurrent neural network (RNN), generative adversarial networks(GAN), or the like.

FIG. 2 is a block diagram illustrating an example configuration of anexample electronic apparatus according to an embodiment of thedisclosure. As illustrated in FIG. 2, the electronic apparatus 100 mayinclude a memory 110 and a processor (e.g., including processingcircuitry) 120.

The memory 110 may store commands or data related to at least one othercomponent of the electronic apparatus 100. For example, the memory 110may be implemented as a non-volatile memory, a volatile memory, aflash-memory, a hard disc drive (HDD) or a solid state drive (SSD), etc.Further, the memory 110 may be accessed by the processor 120, andreading/recording/correcting/deleting/updating, etc. of data by theprocessor 120 may be performed. In the disclosure, the term memory mayinclude a memory 110, a ROM (not shown) inside the processor 120, a RAM(not shown), or a memory card (not shown) (e.g., a micro SD card, amemory stick) installed on the electronic apparatus 100. Also, in thememory 110, programs and data, etc. for constituting various types ofscreens to be displayed in a display area of the display 150 may bestored.

The memory 110 may store a dialog system providing a response to a userinput (for example, a user utterance). A dialog system may include, forexample, and without limitation, an Automatic Speech Recognition (ASR)part (e.g., including processing circuitry and/or executable programelements) 410, a Natural Language Understanding (NLU) part (e.g.,including processing circuitry and/or executable program elements) 420,a Dialog Manager (DM) (e.g., including processing circuitry and/orexecutable program elements) 430, a Natural Language Generating (NLG)part (e.g., including processing circuitry and/or executable programelements) 440, a Text-to-Speech (TTS) (e.g., including processingcircuitry and/or executable program elements) 450, and a KnowledgeDatabase 460, as illustrated in FIG. 4.

The Automatic Speech Recognition part 410 may include various processingcircuitry and/or executable program elements for converting a userutterance into the form of a text that the electronic apparatus 100 canprocess, by performing voice recognition for the user utterance inputthrough a microphone, etc. The Automatic Speech Recognition part 410 mayinclude a language model for correcting a conversion error, a uniqueutterance of a user, an utterance error, etc. The Natural LanguageUnderstanding part 420 may include various processing circuitry and/orexecutable program elements for identifying a task related to an entityand an intent of a user utterance based on a result of voicerecognition. For example, the Natural Language Understanding part 420may perform sentence analysis by interpreting a sentence throughanalysis of the structure and main components of the sentence and usingstatistics/analysis, etc. The Dialog Manager 430 may include variousprocessing circuitry and/or executable program elements for acquiringinformation on a response to a user utterance based on a result ofnatural language understanding and data stored in the Knowledge Database460. The Dialog Manager 430 may be implemented, for example, on a Framebasis, an Agent basis, etc., and may be implemented through modelingbased on a Markov Decision Process (MDP), and Reinforcement Learning.The Dialog Manager 430 may acquire information for generating aresponse, and as described above, acquired information may be determinedbased on a task identified through the Natural Language Understandingpart 420 and data stored in the Knowledge Database 460. The NaturalLanguage Generating part 440 may include various processing circuitryand/or executable program elements and may acquire a natural language asa response to a user utterance based on information acquired through theDialog Manager 430. The TTS 450 may include various processing circuitryand/or executable program elements and convert the acquired naturallanguage into a voice. By the above, the dialog system may provide aresponse to a user utterance as a voice, and a user may perform a dialogwith the electronic apparatus 100.

For example, the Natural Language Generating part 440 according to anembodiment of the disclosure may input information acquired through theDialog Manager 430 and the Knowledge Database 460 as an input value ofan artificial intelligence model, and acquire a natural language as aresponse to a user utterance.

The knowledge Database 460 may, for example, store information necessaryfor generating a response at the Dialog Manager 430. Data stored in theknowledge Database 460 may be diverse. For example, the knowledgeDatabase 460 may store user preference information. User preferenceinformation may refer, for example, to a response type that a userprefers regarding a user utterance. A response type that a user prefersmay be, for example, a response type providing a response to a userutterance or a response type providing an additional inquiry regarding auser utterance. A response type that a user prefers may includeinformation for setting details of a response (or an inquiry) for eachof a response type providing a response to a user utterance or aresponse type providing an additional inquiry regarding a userutterance. For example, user preference information may be a type ofproviding an additional inquiry regarding a user utterance, and anadditional inquiry may be information on an inquiry constituted in theform of Yes or No. User preference information may, for example, be atype of providing an additional inquiry regarding a user utterance, andan additional inquiry may be information on an inquiry constituted toselect one of a plurality of alternatives. User preference informationmay be a type of providing a response to a user utterance, andinformation related to the number of pieces of information included in aresponse.

The knowledge Database 460 may store the past utterance history of auser. For example, the knowledge Database 460 may store historyinformation related to a past user utterance and a response to theutterance, and a re-utterance regarding the response. Methods of storinghistory information may be diverse. For example, history information maybe include ontology including information on the degree of associationamong a plurality of tasks regarding a plurality of user utterances.History information may be in a form wherein information on a userutterance, a response to the utterance, and a re-utterance regarding theresponse is stored in the form of a data set (e.g., (an inquiry, aresponse)).

The memory 110 may store an artificial intelligence agent for operatinga dialog system. For example, the electronic apparatus 100 may use anartificial intelligence agent for generating a natural language as aresponse to a user utterance. An artificial intelligence agent mayrefer, for example, to a dedicated program for providing artificialintelligence (AI) based services (e.g., a voice recognition service, anagent service, a translation service, a search service, etc.), and maybe executed, for example, and without limitation, by a conventionalgeneric-purpose processor (e.g., a CPU), or a separate AI-dedicatedprocessor (e.g., a GPU, etc.), or the like.

For example, where a user utterance is input, an artificial intelligenceagent may operate. An artificial intelligence agent may input a userinquiry into a trained artificial intelligence learning model andacquire a response. If a user utterance (for example, a trigger voicefor executing an artificial intelligence function) is input or apredetermined button (e.g., a button for executing an artificialintelligence agent function) is selected, an artificial intelligenceagent may operate. An artificial intelligence agent may have alreadybeen executed before a user utterance was input or a predeterminedbutton was selected. In this example, after a user utterance is input ora predetermined button is selected, an artificial intelligence agent ofthe electronic apparatus 100 may acquire a natural language as aresponse to the user utterance. An artificial intelligence agent may bein a stand-by state before a user utterance is input or a predeterminedbutton is selected. A stand-by state may refer, for example, to a statewherein receipt of a predefined user input is detected for controllingthe start of an operation of an artificial intelligence agent. If a userutterance is input or a predetermined button is selected while anartificial intelligence agent is in a stand-by state, the electronicapparatus 100 may operate the artificial intelligence agent, and acquirea natural language as a response to the user utterance.

According to an embodiment of the disclosure, the memory 110 may storean artificial intelligence model trained to generate (or acquire) anatural language. In the disclosure, a trained artificial intelligencemodel may be constructed in consideration of a field to which arecognition model is applied or the computer performance of anapparatus, etc. For example, an artificial intelligence model may betrained to acquire a natural language using information acquired fromthe conversation manager 430 and the knowledge database 460 as inputdata. For generating a natural language that is natural, a trainedartificial intelligence model may be a model based on a neural network.An artificial intelligence model may, for example, be designed tosimulate a brain structure of a human on a computer, and may include aplurality of network nodes having weights that simulate neurons of aneural network of a human. The plurality of network nodes may each forma connection relationship so as to simulate synaptic activities ofneurons exchanging signals via synapses. Further, a document summarymodel may include, for example, a neural network model, or a deeplearning model developed from a neural network model. In a deep learningmodel, a plurality of network nodes may be located in different depths(or, layers) from one another, and exchange data according to arelationship of convolution connection. Examples of a trained artificialintelligence model, may include, for example, and without limitation, aDeep Neural Network (DNN), a Recurrent Neural Network (RNN), aBidirectional Recurrent Deep Neural Network (BRDNN), and the like, but atrained artificial intelligence model is not limited thereto.

In the aforementioned embodiment, it was described that an artificialintelligence model is stored in the electronic apparatus 100, but thisis merely an example, and an artificial intelligence model may be storedin another electronic apparatus. For example, an artificial intelligencemodel may be stored in at least one external server. The electronicapparatus 100 can receive input of a user utterance and transmit theutterance to an external server storing an artificial intelligencemodel, and the artificial intelligence model stored in the externalserver can input the user utterance received from the electronicapparatus 100 as an input value and output a result.

The processor 120 may include various processing circuitry and beelectronically connected with the memory 110 and control the overalloperations and functions of the electronic apparatus 100.

For example, the processor 120 may receive input of a user utterance,and determine a task related to the user utterance based on the inputuser utterance. A task related to a user utterance may, for example,include information necessary for a response to a user utterance. Forexample, where a user utterance is an inquiry inquiring about theweather, the task may be a weather check. In an example in which a userutterance is an inquiry inquiring about the temperature, the task may bea temperature check. In an example in which a user utterance is aninquiry inquiring about the degree of atmospheric pollution, the taskmay be an atmospheric pollution check.

The processor 120 may determine a task related to a user utterance and aresponse pattern for an additional task related to the task based on atask related to a user utterance. For example, the processor 120 mayidentify a first task for a first user utterance and a second task for asecond user utterance. If the degree of association between the firsttask and the second task satisfies a predetermined condition, theprocessor 120 may set a response pattern for the first utterance basedon the second task. For example, where the first task is associated withthe second task, the processor 120 may determine a response patternbased on the first task and the second task. A predetermined conditionmay, for example, be a condition wherein information on the degree ofassociation between the first task and the second task is equal to orgreater than a threshold value.

A response pattern may be determined by various methods. For example, aresponse pattern may include at least one of information related to thelength of a response to a user utterance or information related to thetypes of information included in the response to the user utterance, andmay be determined based on the aforementioned information. A responsepattern may be determined based on utterance history including a userutterance and an additional utterance for performing an additional taskand user preference information. An additional task (or a second task)may be a task related to a determined task (or a first task). Forexample, where a determined task is a temperature check, an additionaltask may be a weather check, an atmospheric pollution check, etc. havinga high degree of association with a temperature check.

A response pattern may be a response pattern selected by a user command,or a response pattern automatically generated based on utterancehistory. For example, where there is a user command with a conditionthat a first utterance and a second utterance are input within apredetermined time period, if the second utterance is input within apredetermined time after the first utterance is input, the processor 120may determine that the first task for the first utterance and the secondtask for the second utterance are associated with each other, anddetermine a response pattern. If a user command for a utterance for apredetermined category is input, the processor 120 may determine thatthe first task for the first utterance and the second task for thesecond utterance with respect to the category are associated with eachother, and determine a response pattern. Where a user command isassociated with the length and type of a response, the processor 120 maydetermine a response pattern based on the length of a response or thetype of a response determined according to the user command. Theprocessor 120 may determine a response pattern according to theaforementioned various user commands, but the disclosure is not limitedthereto, and a response pattern can be set automatically.

The processor 120 may acquire response information for a task related toa user utterance and response information for an additional task, andprovide the acquired response information and additional responseinformation to the user. For example, the processor 120 may provide aresponse for a determined response pattern based on the first task andthe second task.

The processor 120 may store information on the degree of associationbetween a task and an additional task in the memory 110. For example,the processor 120 may store information on the degree of associationbetween a task and an additional task in the knowledge database 460.

An additional task (or a second task) may be a task of which degree ofassociation with a task determined according to a user utterance isgreater than a threshold value. For example, the processor 120 maydetermine a task of which information on the degree of association witha task determined according to a user utterance is greater than athreshold value as an additional task. For example, where a taskdetermined according to a user utterance is a temperature check, and thedegree of association between the temperature check task and a weathercheck task is 0.9, and the degree of association between the temperaturecheck task and an atmospheric pollution check task is 0.5, and thethreshold value is 0.7, the processor 120 may determine the weathercheck task of which degree of association with the temperature checktask is greater than 0.7 as an additional task, and may not determinethe atmospheric pollution check task of which degree of association withthe temperature check task is smaller than 0.7 as an additional task.

The processor 120 may receive input of a third utterance of a user,identify a third task for the third utterance, and determine the degreeof association between the identified third task and the first task. Ifthe degree of association between the first task and the third tasksatisfies a predetermined condition, the processor 120 may determine thepriorities of the second task and the third task, and set a responsepattern for the first utterance based on the determined priorities, thesecond task, and the third task.

For example, where a plurality of additional tasks are determined, theprocessor 120 may determine information on the degree of associationbetween a task for a user utterance and the plurality of determinedtasks, and determine an order of providing responses to additional tasksbased on the determined information on the degree of association. Forexample, where a task for a user utterance is a temperature check task,and determined additional tasks are a weather check task, an atmosphericpollution check task, and a population density check, and the degree ofassociation between the temperature check task and the weather checktask is 0.9, and the degree of association between the temperature checktask and the atmospheric pollution check task is 0.5, and the degree ofassociation between the temperature check task and the populationdensity check task is 0.3, the processor 120 may provide an additionalresponse for a task having a high degree of association first. Forexample, with respect to a user utterance “What is the temperature ofSeoul today?”, the processor 120 may provide responses in the order of aresponse for the temperature check task for the user utterance, anadditional response for the weather check task, an additional responsefor the atmospheric pollution check task, and an additional response forthe population density check task, like “The temperature of Seoul todayis 20 degrees, the weather is fine, the concentration of fine dust is10, and the population density is low”.

According to another embodiment of the disclosure, where a responsepattern set for a first task for a first utterance is set based on asecond task associated with the first task, if a third utterancedifferent from the first and second utterances is input, the processor120 may determine whether a third task for the third utterance isassociated with the first task for the first utterance, and if there isassociation, the processor 120 may provide a response according to aresponse pattern set for the first task.

The processor 120 may acquire a feedback for the provided response, andupdate the degree of association between the first task and the secondtask based on the acquired feedback. For example, the processor 120 mayacquire a user feedback for the response information and additionalresponse information provided for a user utterance, and updateinformation on the degree of association between a task and anadditional task for the user utterance based on the acquired feedback.For example, while, with respect to a user utterance “What is thetemperature of Seoul today?”, a response such as “The temperature ofSeoul today is 20 degrees, the weather is fine, the concentration offine dust is fine at 10, and the population density is low” is beingprovided, if a user feedback ending responses after weather informationis input, the processor 120 may decrease the degree of associationbetween the temperature check task and the atmospheric pollution checktask and the degree of association between the temperature check taskand the population density check task.

The processor 120 may receive input of an additional user utteranceregarding response information and additional response informationprovided for a user utterance, and update information on the degree ofassociation between a task for the user utterance and a task for theadditional user utterance based on a task for the input additional userutterance. For example, if, with respect to a user utterance “What isthe temperature of Seoul today?”, a response such as “The temperature ofSeoul today is 20 degrees, and the weather is fine” is provided, andthen an additional user utterance such as “How is the concentration offine dust in Seoul today?” is input, the processor 120 may provide aresponse such as “Today's concentration of fine dust in Seoul is fine as10”, and update information on the degree of association between thetemperature check task and the atmospheric pollution check task.

The processor 120 may provide response information for a user utterance,and provide an inquiry message for providing additional responseinformation. That is, the processor 120 may not provide responseinformation and additional response information for a user utterancetogether, but provide an inquiry message inquiring whether to beprovided additional response information. For example, if a userutterance “What is the temperature of Seoul today?” is input, theprocessor 120 may provide a response to the user utterance and aninquiry message such as “The temperature of Seoul today is 20 degrees.Would you like to know about the weather of Seoul, too?”. If a usercommand for the inquiry message is received, the processor 120 mayprovide an additional response for the user command. For example, theprocessor 120 may provide a response to the user utterance and aninquiry message for providing additional response information such as“Would you like to know about the weather of Seoul, too?”. If theprocessor 120 receives a user command “Yes” to the user inquiry “Wouldyou like to know about the weather of Seoul, too?”, the processor 120may provide an additional response like “Today's weather of Seoul isfine”.

The processor 120 may store a task for a user utterance and anadditional task related to the task in the form of, for example,ontology in the memory 110 based on information on the degree ofassociation between a task for a user utterance and an additional taskrelated to the task for the user utterance. For example, the processor120 may store a first task and a second task in the form of ontology inthe memory 110 based on the degree of association between the first taskand the second task.

The aforementioned various operations of the processor 120 may beperformed by an artificial intelligence model. For example, at least onestep among an operation of determining a task related to a userutterance, an operation of determining a response pattern, an operationof acquiring additional response information, and an operation ofproviding additional response information is performed by an artificialintelligence model, and the artificial intelligence model may include aplurality of layers respectively including at least one node, and eachof the at least one node may be a neural network model having aconnection weight for interpreting an input value.

FIG. 3 is a block diagram illustrating an example configuration of anexample electronic apparatus according to an embodiment of thedisclosure.

As illustrated in FIG. 3, the electronic apparatus 100 may furtherinclude a communicator (e.g., including communication circuitry) 130, aninputter (e.g., including input circuitry) 140, a display 150, and anaudio outputter (e.g., including audio output circuitry) 160, inaddition to the memory 110 and the processor 120. However, components ofthe electronic apparatus 100 are not limited to the aforementionedcomponents, and some components may be added or omitted depending onneeds.

The communicator 130 may include various communication circuitry andcommunication with an external apparatus. A communicative connectionbetween the communicator 130 and an external apparatus may includecommunication through a third apparatus (e.g., a repeater, a hub, anaccess point, a server, or a gateway). Wireless communication mayinclude, for example, and without limitation, cellular communicationusing at least one of LTE, LTE Advance (LTE-A), code division multipleaccess (CDMA), wideband CDMA (WCDMA), a universal mobiletelecommunications system (UMTS), Wireless Broadband (WiBro), a GlobalSystem for Mobile Communications (GSM), or the like. According to anembodiment, wireless communication may include, for example, at leastone of wireless fidelity (WiFi), Bluetooth, Bluetooth low energy (BLE),Zigbee, near field communication (NFC), Magnetic Secure Transmission,radio frequency (RF), or a body area network (BAN). Wired communicationmay include, for example, at least one of a universal serial bus (USB),a high definition multimedia interface (HDMI), a recommended standard232 (RS-232), power line communication, or a plain old telephone service(POTS). Networks wherein wireless communication or wired communicationis performed may include at least one of a telecommunication network,for example, a computer network (e.g.: a LAN or a WAN), the Internet, ora telephone network.

The inputter 140 may include various input circuitry and receive inputof a user command. The inputter 140 may include, for example, andwithout limitation, a camera 141, a microphone 142, a touch panel 143,or the like.

The camera 141 may include various circuitry for acquiring image dataaround the electronic apparatus 100. The camera 141 may photograph astill image and a moving image. For example, the camera 141 may includeone or more of an image sensor (e.g.: a front surface sensor or a rearsurface sensor), a lens, an image signal processor (ISP), or flash(e.g.: an LED or a xenon lamp, etc.). The microphone 142 may includevarious circuitry for acquiring sound around the electronic apparatus100. The microphone 142 may receive input of an external acoustic signaland generate electronic voice information. The microphone 142 may usevarious noise removal algorithms for removing noises generated in aprocess of receiving input of an external acoustic signal. Imageinformation or voice information input through the camera 141 or themicrophone 142 may be input as an input value of an artificialintelligence model.

The touch panel 143 may include various circuitry and receive input ofvarious user inputs. The touch panel 143 may receive input of data by auser manipulation. Also, the touch panel 143 may be included incombination with a display that will be described later.

The inputter 140 may be various components for receiving input ofvarious data in addition to the aforementioned camera 141, microphone142, and touch panel 143.

The display 150 may include a component for outputting various images.The display 150 for providing various images may be implemented asdisplay panels in various forms. For example, display panels may beimplemented as various display technologies such as, for example, andwithout limitation, a Liquid Crystal Display (LCD), Organic LightEmitting Diodes (OLEDs), Active-Matrix Organic Light-Emitting Diodes(AM-OLEDs), Liquid Crystal on Silicon (LcoS), Digital Light Processing(DLP), etc. The display 150 may be coupled to at least one of the frontsurface area, the side surface area, or the rear surface area of theelectronic apparatus 100 in the form of a flexible display.

The audio outputter 160 may include various audio output circuitry foroutputting not only various kinds of audio data for which variousprocessing works such as decoding or amplification, and noise filteringwere performed by an audio processor, but also various kinds ofnotification sounds or voice messages. The audio processor is acomponent performing processing of audio data. At the audio processor,various processing such as decoding or amplification, and noisefiltering of audio data may be performed. Audio data processed at theaudio processor 150 may be output to the audio outputter 160. Inparticular, the audio outputter may be implemented as a speaker, butthis is merely an example, and the audio outputter may be implemented asan output terminal that can output audio data.

The processor 120 may include various processing circuitry and controlsthe overall operations of the electronic apparatus 100, as describedabove. The processor 120 may include a RAM 121, a ROM 122, a main CPU124, a graphic processor 123, first to nth interfaces 125-1 to 125-n,and a bus 126. The RAM 121, the ROM 122, the main CPU 124, the graphicprocessor 123, and the first to nth interfaces 125-1 to 125-n, etc. maybe connected with one another through the bus 126.

The ROM 122 stores a set of instructions, etc. for system booting. Whena turn-on instruction is input and power is supplied, the main CPU 124copies the O/S stored in the memory in the RAM 121 according to theinstruction stored in the ROM 122, and boots the system by executing theO/S. When booting is completed, the main CPU 124 copies various types ofapplication programs stored in the memory in the RAM 121, and performsvarious types of operations by executing the application programs copiedin the RAM 121.

The main CPU 124 accesses the memory 110, and performs booting using theO/S stored in the memory 110. The main CPU 124 performs variousoperations using various types of programs, contents, data, etc. storedin the memory 110.

The first to nth interfaces 125-1 to 125-n may be connected with theaforementioned various types of components. One of the interfaces may bea network interface connected with an external apparatus through anetwork.

FIG. 5A is a flowchart illustrating an example method for providing aresponse to a user utterance according to an embodiment of thedisclosure.

When a user utterance is input, the electronic apparatus may determine atask associated with the input user utterance at operation S510. Forexample, the electronic apparatus 100 may acquire the entity and theintention of a user utterance input through the natural languageunderstanding part 420 and determine a task for the user utterance.According to an embodiment of the disclosure, the electronic apparatus100 may acquire a dialog act, a main act, and an entity in a userutterance through the natural language understanding part 420. A dialogact may refer, for example, to an intentional act of a speaker forperforming the purpose of the dialog included in the utterance, andindicates whether the utterance of the user is a request of an act(Request), a request of the value of a certain variable to the listenerby the speaker (WH-Question), or a request of an answer in YES/NO to thelistener by the speaker (YN-Question), or provision of information tothe listener by the speaker (inform), etc. Also, a main act refers tosemantic information indicating an act desired by the utterance througha dialog in a specific domain. In addition, an entity is informationadded for specifying the meaning of an act intended in a specificdomain.

For example, where a user utterance is “How's the weather today?”, thedialog act in the user utterance may be ‘a WH-Question’, the main actmay be ‘a weather check’, and the entity may be ‘today’. Accordingly, atask for the user utterance may be determined as checking today'sweather and providing a response.

The electronic apparatus 100 may determine additional information thatis not included in the user utterance but is associated with the mainact as an entity. Information on which additional information isassociated with the main act may be stored in advance in the electronicapparatus 100. For example, where additional information associated with‘a weather check’ which is the main act is the current location of theelectronic apparatus 100, the electronic apparatus 100 may determine thecurrent location of the electronic apparatus 100 (for example,‘Yangjae-dong’) as the entity, although it is not included in the userutterance “How's the weather today?”. The electronic apparatus 100 maydetermine a task for the user utterance as checking Yangjae-dong'sweather today and providing a response based on ‘today’ which is anentity acquired from the user utterance and ‘Yangjae-dong’ which is anentity acquired from additional information associated with the mainact.

The electronic apparatus 100 may determine an additional task related tothe determined task at operation S520. Specifically, the electronicapparatus 100 may determine an additional task related to the determinedtask based, for example, on the past user utterance history informationand user preference information. For example, where the determined taskis a weather check task, the electronic apparatus 100 may determine atemperature check task and an atmospheric pollution check task asadditional tasks based on the user utterance history and user preferenceinformation.

The electronic apparatus 100 may determine additional responseinformation based on information on the degree of association betweenthe determined task and additional tasks at operation S530. For example,the electronic apparatus 100 may determine a task of which degree ofassociation is greater than a threshold value as an additional task,among a plurality of additional tasks related to the determined task.For example, if a task determined according to a user utterance is atemperature check, and the degree of association between the temperaturecheck task and a weather check task is 0.9, and the degree ofassociation between the temperature check task and an atmosphericpollution check task is 0.5, and the threshold value is 0.7, theelectronic apparatus 100 may determine the weather check task of whichdegree of association with the temperature check task is greater than0.7 as an additional task, and may not determine the atmosphericpollution check task of which degree of association with the temperaturecheck task is smaller than 0.7 as an additional task. A threshold valuemay be determined by various methods. As an example, a threshold valuemay be determined by user setting. As another example, a threshold valuemay be output by inputting a user utterance and a response to the userutterance into a trained artificial intelligence model as an inputvalue.

In addition, the electronic apparatus 100 may provide responseinformation and additional response information for the determined taskat operation S540. As an example, the natural language generating part440 of the electronic apparatus 100 may generate a response to a userutterance as a natural language based on response information andadditional response information for a determined task and provide theresponse. However, the disclosure is not limited thereto, and theelectronic apparatus 100 can provide response information and additionalresponse information for a determined task in the form of a text.

If a response for a user utterance is provided, the electronic apparatus100 may update the knowledge database 460 based on the input userutterance and additional response information for the user utterance atoperation S550. For example, the electronic apparatus 100 may input aninput user utterance and additional response information for the userutterance into a trained artificial intelligence model as an inputvalue, and update information on the degree of association among aplurality of tasks, and store the updated information on the degree ofassociation in the knowledge database 460. As another example, theelectronic apparatus 100 may input an input user utterance andadditional response information for the user utterance into a trainedartificial intelligence model as an input value, and update a thresholdvalue for a task related to the user utterance, and update the updatedthreshold value in the knowledge database 460.

FIG. 5B is a diagram illustrating an example method for providing anadditional response according to another embodiment of the disclosure.

For example, the electronic apparatus 100 may provide responseinformation to a user utterance, and provide an inquiry message forproviding additional response information. For example, the electronicapparatus 100 may not provide response information and additionalresponse information for a user utterance together, but provide aninquiry message inquiring whether to be provided additional responseinformation. For example, if a user utterance “What is the temperatureof Seoul today?” is input, the electronic apparatus 100 may provide aresponse to the user utterance and an inquiry message such as “Thetemperature of Seoul today is 20 degrees. Would you like to know aboutthe weather of Seoul, too?”. If a user command for the inquiry messageis received, the electronic apparatus 100 may provide an additionalresponse for the user command That is, the electronic apparatus 100 mayprovide a response to the user utterance and an inquiry message forproviding additional response information such as “Would you like toknow about the weather of Seoul, too?”. If the electronic apparatus 100receives a user command “Yes” to the user inquiry “Would you like toknow about the weather of Seoul, too?”, the electronic apparatus 100 mayprovide an additional response like “Today's weather of Seoul is fine”.

FIG. 6A is a diagram illustrating an example method for updatinginformation on the degree of association among tasks according to anembodiment of the disclosure, and FIG. 6B is a diagram illustrating anexample regarding the information on the degree of association amongtasks stored in the knowledge database 460 according to an embodiment ofthe disclosure.

As illustrated in FIG. 6A, the electronic apparatus 100 may updateinformation on the degree of association among a temperature check task,a weather check task, an atmospheric pollution check task, and apopulation density check task based on the user utterance history anduser preference information.

For example, as illustrated in the left side of FIG. 6A, in the initialstate, the degree of association among each task may be 0. In thisexample, the electronic apparatus 100 may provide temperatureinformation for a user utterance requesting a temperature check, provideweather information for a user utterance requesting a weather check,provide atmospheric pollution information for a user utterancerequesting an atmospheric pollution check, and provide populationdensity information for a user utterance requesting a population densitycheck.

When user utterances and response information for user utterances areaccumulated in the knowledge database 460, the electronic apparatus 100may update information on the degree of association among each taskbased on the accumulated data. As an example, the electronic apparatus100 may update information on the degree of association among each taskbased on information on associated tasks for each of the same entitiesin a user utterance. For example, if a user utterance related to aplurality of tasks (a temperature check, a weather check, an atmosphericpollution check, etc.) for the same entity (place) is input, theelectronic apparatus 100 may update information on the degree ofassociation among each of the plurality of tasks for the same entity.

As another example, if there is an additional user utterance for aresponse to a user utterance, the electronic apparatus 100 may updateinformation on the degree of association among each task based oninformation on a task for the user utterance and information on a taskfor the additional user utterance. For example, if there are a responseto a user utterance and an additional user utterance, the electronicapparatus 100 may increase the degree of association between a task forthe user utterance and a task for the additional user utterance. Forexample, after the electronic apparatus 100 provides a responseinforming the temperature with respect to a user utterance requesting atemperature check, if an additional user utterance requesting a weathercheck is input, the electronic apparatus 100 may heighten the degree ofassociation between a temperature check task and a weather check task.The electronic apparatus 100 can update task information inconsideration of the order that the user utterance and the additionaluser utterance were input. For example, if a task for a user utteranceand tasks for additional utterances are input in the order of atemperature check task, a weather check task, and an atmosphericpollution check task, the electronic apparatus 100 may set the degree ofassociation between the temperature check task and the weather checktask to be higher than the degree of association between the temperaturecheck task and the atmospheric pollution check task.

As another example, the electronic apparatus 100 may update informationamong each task based on a feedback for a response to a user utterance.For example, while a response to a user utterance is being provided, ifa user command stopping the response is input, the electronic apparatus100 may decrease the degree of association between a task correspondingto the response that was being provided after the user command stoppingthe response was input and a task corresponding to the user utterance.As an example, a case wherein a response that the electronic apparatus100 will provide to a user utterance “What is the temperature today?” is“Today's temperature is 20 degrees and the weather is fine” can beassumed. Where the electronic apparatus 100 provides a responseregarding today's temperature, and while the electronic apparatus 100 isproviding a response regarding the weather, a user command stopping theresponse is input and the response regarding the weather cannot beprovided, the electronic apparatus 100 may decrease the degree ofassociation between the temperature check task and the weather checktask.

The electronic apparatus 100 can update the degree of association amongtasks based on the user utterance history and user preferenceinformation, and can also update a threshold value for determining anadditional task.

In conclusion, the electronic apparatus 100 may update information onthe degree of association among each task and store the information inthe knowledge database 460 as in FIG. 6A, based on history informationon the user utterance and user preference information. For example, asshown in FIG. 6B, the information on the degree of association between“weather” and “fine/cloudy . . . , UV rays, temperature, humidity, airquality and tomorrow's weather”, the information on the degree ofassociation “tomorrow's weather” and “tomorrow's temperature, tomorrow'sair quality and tomorrow's humidity” and the information on the degreeof association between “place” and “travel site, weather, lodging,traffic, famous restaurant and city information” may be stored in theknowledge database 460 according to the various embodiments of thedisclosure as described above.

FIG. 7 is a diagram illustrating an example of providing a response to auser utterance according to another embodiment of the disclosure.

As illustrated in FIG. 7, if a user utterance is input, the electronicapparatus 100 may analyze the user utterance and generate a response forthe user utterance. The electronic apparatus 100 may store the userutterance and information related to the user utterance in the form of atable. For example, the stored table may include information on topicsof inquiries (e.g., weather information, the traffic schedule, etc.),the frequency of inquiries (e.g., n times per day, etc.), the averagetimes of continuous inquiries (the times of additional inquiriesregarding a user utterance), and detailed contents of inquiries.

For example, the electronic apparatus 100 may store not only a responseto a user utterance, but also match an additional user utterance andresponse information regarding the additional user utterance and storethem. If a user utterance is input after a table for various userutterance history is acquired, the electronic apparatus 100 maydetermine a response for the user utterance based on the table regardingthe various user utterance history.

FIGS. 8A, 8B, 9, 10 and 11 are diagrams illustrating an example in whicha response to a user utterance is an inquiry type according to anotherembodiment of the disclosure.

FIGS. 5A, 5B, 6 and 7 were described as an example in which the responsetype of the electronic apparatus 100 to a user utterance is a responseprovision type, but the disclosure is not limited thereto. For example,where there is an unclear part in a user utterance, the electronicapparatus 100 may not provide an answer to the user utterance, butprovide a response inquiring about an insufficient part regarding theuser utterance. For example, if, as a result of analyzing a userutterance, there is an omitted part in an entity or a task, or there area plurality of tasks for the user utterance, the electronic apparatus100 may provide an inquiry requesting an omitted task or entity orprovide an inquiry inquiring which task is to be selected among aplurality of tasks. For example, where a user utterance such as “How'sthe weather today?” is input, the electronic apparatus 100 may determinethat an entity related to a place is omitted, and provide a responsesuch as “Would you like to know about the weather of Seoul today?”.Where a user utterance such as “Call Saebom” is input (see, e.g., FIG.8A), the electronic apparatus 100 may determine that there are aplurality of “Saeboms” searched, and provide a response such as “Whoshould I call among Saebom Choi, Saebom Lee, and Saebom Park?”.

If a response type for a user utterance is an inquiry provision type,the electronic apparatus 100 may provide one of a selection type ofreceiving selection of one of a plurality of alternatives regarding theuser utterance or a Yes/No type of inquiring which one among a pluralityof alternatives is correct as a response to the user utterance.

For example, if a user utterance such as “Call Saebom” is input, theelectronic apparatus 100 may decide a response to the user utterance asan inquiry provision type. As illustrated in FIG. 8A, the electronicapparatus 100 may provide a response to a user utterance as a selectiontype for receiving selection of one of a plurality of alternatives(Saebom Choi, Saebom Lee, and Saebom Park) such as “Three Saeboms weresearched. Who should I call among Saebom Choi, Saebom Lee, and SaebomPark?”. As illustrated in FIG. 8B, the electronic apparatus 100 mayprovide a response to a user utterance as a Yes/No type of determiningone of a plurality of alternatives, and inquiring whether the determinedalternative is correct, like “Should I call Saebom Lee?”.

The electronic apparatus 100 may decide whether to provide a response asa selection type, or provide a response as a Yes/No type by variousmethods. As an example, the electronic apparatus 100 may decide aresponse provision type according to the user setting. For example, theelectronic apparatus 100 may provide a response as a selection type if auser sets to provide a response as a selection type, and provide aresponse as a Yes/No type if a user sets to provide a response as aYes/No type. As another example, the electronic apparatus 100 maydetermine a task for a user utterance, determine a plurality ofalternatives for the determined task, and decide one type between aselection type and a Yes/No type based on information on the degree ofassociation between the determined task and the plurality ofalternatives and the threshold value. For example, if a user utterancesuch as “Call Saebom” is input, the electronic apparatus 100 may acquirea call sending task for the user utterance, and acquire a plurality ofalternatives (Saebom Choi, Saebom Lee, and Saebom Park) for the subjectto send a call. Where the degree of association between the call sendingtask and Saebom Choi is 0.1, and the degree of association between thecall sending task and Saebom Lee is 0.9, and the degree of associationbetween the call sending task and Saebom Park is 0.5, and the thresholdvalue is 0.7, as there is one alternative greater than the thresholdvalue (Saebom Lee), the electronic apparatus 100 may provide a responsesuch as “Should I call Saebom Lee?” (a Yes/No type). Where the degree ofassociation between the call sending task and Saebom Choi is 0.1, andthe degree of association between the call sending task and Saebom Leeis 0.9, and the degree of association between the call sending task andSaebom Park is 0.5, and the threshold value is 0.3, as there are aplurality of alternatives greater than the threshold value (Saebom Lee,Saebom Park), the electronic apparatus 100 may provide a response suchas “Who should I call between Saebom Lee and Saebom Park?” (a selectiontype). As still another example, where the degree of association betweenthe call sending task and Saebom Choi is 0.1, and the degree ofassociation between the call sending task and Saebom Lee is 0.9, and thedegree of association between the call sending task and Saebom Park is0.5, and the threshold value is 0.3, the electronic apparatus 100 mayprovide a response such as “Who should I call among Saebom Choi, SaebomLee, and Saebom Park?” (a selection type). For example, if there are aplurality of alternatives greater than a threshold value, the electronicapparatus 100 can provide an alternative smaller than the thresholdvalue (Saebom Park) as a response.

FIG. 9 is a flowchart illustrating an example method for providing aninquiry type response to a user utterance according to an embodiment ofthe disclosure.

When a user utterance is input, the electronic apparatus 100 maydetermine a task related to the input user utterance at operation S910.For example, the electronic apparatus 100 may acquire an entity and atask of a user utterance input through the natural languageunderstanding part 420 and determine a task for the user utterance. Asan example, where a user utterance is “Call Saebom,” the determined taskmay be sending of a call.

The electronic apparatus 100 may determine an additional inquiryincluded in the determine response information based on the determinedtask at operation S920. For example, there may be a plurality of piecesof information necessary for performing the task. For example, wherethere are a plurality of pieces of information necessary for performingthe task, the electronic apparatus 100 may determine an additionalinquiry for determining one of a plurality of pieces of information. Forexample, an additional inquiry may be a selection type such as “ThreeSaeboms were searched. Who should I call among Saebom Choi, Saebom Lee,and Saebom Park?”, or a Yes/No type such as “Should I call Saebom Lee?”.

The electronic apparatus 100 may determine additional responseinformation based on information on the degree of association betweenthe determined task and the additional inquiry at operation S930. Forexample, the electronic apparatus 100 may determine a task for a userutterance, determine a plurality of pieces of information for thedetermined task, and determine one type between a selection type or aYes/No type based on information on the degree of association betweenthe determined task and an additional inquiry decided based on aplurality of alternatives and a threshold value. As described above,where there is one additional inquiry greater than a threshold value,the electronic apparatus 100 may decide a Yes/No type as additionalinformation, and where there are a plurality of additional inquiriesgreater than a threshold value, the electronic apparatus 100 maydetermine selection types as additional inquiries.

The electronic apparatus 100 may provide response information and anadditional inquiry (or an additional response) for the determined taskat operation S940. As an example, the natural language generating part440 of the electronic apparatus 100 may generate a response to a userutterance as a natural language based on response information andadditional response information for the decided task and provide theresponse. However, the disclosure is not limited thereto, and theelectronic apparatus 100 can provide response information and additionalresponse information for the decided task in the form of a text.

Where a response for a user utterance is provided, the electronicapparatus 100 may update the knowledge database 460 based on the inputuser utterance and additional response information (e.g., additionalinquiry) for the user utterance at operation S950. For example, theelectronic apparatus 100 may input the input user utterance andadditional response information for the user utterance into a trainedartificial intelligence model as an input value, and update informationon the degree of association among a plurality of tasks, and store theupdated information on the degree of association in the knowledgedatabase 460. As another example, the electronic apparatus 100 may inputthe input user utterance and additional response information for theuser utterance into a trained artificial intelligence model as an inputvalue, and update a threshold value for a task related to the userutterance, and update the updated threshold value in the knowledgedatabase 460.

FIG. 10 is a diagram illustrating and example of providing a response toa user utterance according to still another embodiment of thedisclosure.

As illustrated in FIG. 10, when a user utterance is input, theelectronic apparatus 100 may analyze the user utterance, and generate aninquiry regarding the user utterance. When an additional user utterancefor the inquiry regarding the user utterance is input, the electronicapparatus 100 may provide a response to the additional user utterance.The electronic apparatus 100 may store the user utterance andinformation related to the user utterance in the form of a table. Forexample, the stored table may include information on the inquiry topic(the counter part of the call, the means of transmitting a message, thesearch engine, etc.), the inquiry type (a selection type, a Yes/No type,etc.), the response type (a selection type, a Yes/No type, etc.), thecontent of the response, the times of generation of inquiries, etc.

For example, the electronic apparatus 100 may match an additionalinquiry regarding a user utterance, a response type for the additionalinquiry, and the content of a response to the additional inquiry, andstore them. When a user utterance is input after a table regardingvarious user utterance history is acquired, the electronic apparatus 100may decide a response to the user utterance based on the table regardingvarious user utterance history.

Where the aforementioned various operations of the electronic apparatus100 are performed by an artificial intelligence model, the electronicapparatus 100 may operate the artificial intelligence model, but thedisclosure is not limited thereto. For example, as illustrated in FIG.11, the operation of the artificial intelligence model may be performedat a server 200.

For example, when a user utterance is input, the electronic apparatus100 may transmit the input user utterance to the server 200. The server200 may determine a task related to the user utterance based on thereceived user utterance, determine a response pattern including the taskrelated to the user utterance and an additional task related to thedetermined task based on the determined task, acquire responseinformation for the task related to the user utterance and additionalresponse information for the additional task, and generate a naturallanguage based on the acquired response information and additionalresponse information and transmit the natural language to the electronicapparatus 100.

According to an embodiment, the electronic apparatus 100 and the server200 may be connected to each other by remote communication.

The electronic apparatus 100 and the server 200 may be connected to eachother by short distance communication (e.g., Wi-Fi, Wi-Fi direct,Bluetooth). The server 200 may, for example, be a device located in auser's home. For example, the server 200 may additionally operate anartificial intelligence model for the electronic apparatus 100 whilepreforming a separate function while being located in a user's home,such as, for example, and without limitation, a TV, a refrigerator, anair conditioner, an AI speaker, or the like.

According to an embodiment, the server 200 may store severalcompositions of the artificial intelligence model. For example, theserver 200 may store only knowledge data base 460 in a conversationsystem of FIG. 4. The server 200 may store a separate knowledge database460 for each user, and based on user information of the electronicapparatus 100 connected to the server, transmit a correspondingknowledge data base 460 of user to the electronic apparatus 100. Theserver 200 may transmit the knowledge data base 460 at regular intervalsto the electronic apparatus 100, or transmit the same to the electronicapparatus 100 whenever the knowledge data base 460 of the server 200 isupdated.

Meanwhile, in FIGS. 8A, 8B, 9, 10 and 11, the electronic apparatus 100providing an additional inquiry for acquiring additional informationwhere there is an unclear part in a user utterance was described.However, even if there is an unclear part in a user utterance, theelectronic apparatus 100 may acquire additional information based onuser context information and provide a response to the user. Forexample, where a user utterance is “How's the weather?”, the electronicapparatus 100 may determine that a place for requesting a weather checkis unclear. Instead of providing an additional inquiry requesting aplace for a weather check to a user, the electronic apparatus 100 maydetermine the current location of the electronic apparatus 100 using GPSinformation, etc., and provide the weather information of the determinedlocation. As another example, where a user utterance is “Call Saebom”,instead of providing an additional inquiry such as “Who should I callamong Saebom Choi, Saebom Lee, and Saebom Park?” or “Should I callSaebom Choi?”, the electronic apparatus 100 may determine that there isno log of having called Saebom Lee and Saebom Park in the past, and callSaebom Choi.

FIG. 12 is a flowchart illustrating an example method of controlling anelectronic apparatus according to an embodiment of the disclosure.

The electronic apparatus 100 may receive a user utterance at operationS1210. The electronic apparatus 100 may determine a task related to theuser utterance based on the received user utterance at operation S1220.For example, where a user utterance is “How's the weather today?”, theelectronic apparatus 100 may determine “a weather check” as a taskrelated to the user utterance. As another example, where a userutterance is “Call Saebom”, the electronic apparatus 100 may determine“sending of a call” as a task related to the user utterance.

The electronic apparatus 100 may determine a task related to the userutterance and an additional task related to the task based on the taskrelated to the user utterance at operation S1230. For example, theelectronic apparatus 100 may determine a task of which degree ofassociation with the task related to the user utterance is greater thana threshold value as an additional task. As described above, the degreeof association with a task related to user utterance and a thresholdvalue may be determined based on user utterance history and userpreference information.

The electronic apparatus 100 may acquire response information for thetask related to the user utterance and response information for theadditional task at operation S1240. The electronic apparatus 100 mayprovide the acquired response information and additional responseinformation at operation S1250.

FIG. 13 is a flowchart illustrating an example method of controlling anelectronic apparatus according to another embodiment of the disclosure.

Referring to FIG. 13, the electronic apparatus 100 may receive input ofa first utterance of a user at operation S1310. The first utterance maybe, for example, “What is the temperature of Umyeon-dong?”.

The electronic apparatus 100 may identify a first task for the firstutterance based on the first utterance at operation S1320. The firsttask may be, for example, a weather check of Umyeon-dong.

The electronic apparatus 100 may provide a response for the first taskaccording to a predetermined response pattern at operation S1330. Forexample, a response for the first task provided according to apredetermined response pattern may be “The weather of Umyeong-dong todayis fine, and warm”.

The electronic apparatus 100 may receive input of a second utterance ofthe user at operation S1340. For example, the second utterance may be“Tell me about the concentration of fine dust in Umeyon-dong”.

The electronic apparatus 100 may identify a second task for the secondutterance based on the second utterance, and determine the degree ofassociation between the first task and the second task at operationS1350.

Separately from determination of the degree of association, theelectronic apparatus 100 may provide a response according to apredetermined response pattern regarding the second task. For example,the response may be “The concentration of fine dust in Umyeon-dong isfine at 10, and the concentration of ultra fine dust is fine at 20”.

If the degree of association determined at operation S1350 satisfies apredetermined condition, the electronic apparatus 100 may set a responsepattern for the first task based on the second task at operation S1360.

According to an embodiment, a response pattern for the first task may beset such that a response pattern for the second task can be additionallyreflected to the response pattern for the first task. After a responsepattern for the first task is set based on the second task as describedabove, if a first utterance is input, the electronic apparatus mayprovide a response according to the set response pattern. For example,when the first utterance which is “What is the temperature ofUmyeon-dong?” is input again, the response pattern for the second taskis additionally reflected, and a response such as “The weather ofUmeyon-dong today is fine, and warm. And the concentration of fine dustis fine at 10, and the concentration of ultra-fine dust is fine at 20”may be provided according to the set response pattern for the firsttask.

According to another embodiment, a response pattern for the second taskmay be set such that a response pattern for the first task can beadditionally reflected to the response pattern for the second task.After a response pattern for the second task is set based on the firsttask as described above, if a second utterance is input, the electronicapparatus may provide a response according to the set response pattern.For example, when the second utterance which is “What is the level offine dust of Umyeon-dong?” is input again, the response pattern for thefirst task is additionally reflected, and a response such as “Theweather of Umeyon-dong today is fine, and warm. And the concentration offine dust is fine at 10, and the concentration of ultra-fine dust isfine at 20” may be provided according to the set response pattern forthe second task.

According to the aforementioned example embodiments, a response is notprovided according to a fixed response pattern, but a response patternmay be adaptively changed as a user's habit, preference, etc. arereflected as the user uses an electronic apparatus, and accordingly,there are effects that a response that suits the intention of the userbetter can be provided, and the user can acquire a desired response atonce without inquiring several times, and the like.

A function related to the artificial intelligence according to thedisclosure may be operated through the processor 120 and the memory 110.

The processor 120 may include, for example, one or a plurality ofprocessors 120. The one or the plurality of processors 120 may include,for example, and without limitation, a general purpose processor, suchas a CPU, an AP, or the like, a graphics dedicated processor, such as aGPU, a VPU or the like, an artificial intelligence processor such as anNPU, or the like.

One or a plurality of processors 120 may control the electronicapparatus 100 to process input data according to a predefined operationrule or an artificial intelligence model stored in the memory 110. Thepredefined operation rule or the artificial intelligence model ischaracterized by being made through training.

Being made through learning may refer, for example, to a predefinedoperation rule of desired feature or an artificial intelligence modelbeing generated by applying a learning algorithm to a plurality oflearning data. Such learning may be made in a device itself that theartificial intelligence is performed according to the disclosure, or bemade through a separate server/system.

The artificial intelligence model may include a plurality of neuralnetwork layers. Each layer may have a plurality of weight values and mayperform calculation of layers through a calculation result of a previouslayer and a calculation of the plurality of weight values. Examples ofneural networks include, for example, and without limitation,convolutional neural network (CNN), deep neural network (DNN), recurrentneural network (RNN), restricted boltzmann machine (RBM), deep beliefnetwork (DBN), bidirectional recurrent deep neural network (BRDNN), deepQ-networks, or the like, and the neural network in the disclosure is notlimited to the above examples.

The learning algorithm may refer, for example, to a method of training apredetermined target device (e.g., a robot) using a plurality oflearning data so that the predetermined target device make a decision ormake a prediction by itself. Examples of the learning algorithm include,for example, and without limitation, supervised learning, unsupervisedlearning, semi-supervised learning or reinforcement learning, learningalgorithms in the disclosure are not limited to the above examplesexcept where specified.

The term “a part” or “a module” used in the disclosure includes a unitincluding hardware, software, or firmware, or any combination thereof,and it may be interchangeably used with terms, for example, logic, alogical block, a component, or a circuit. Also, “a part” or “a module”may be a component including an integrated body or a minimum unitperforming one or more functions or a portion thereof. For example, amodule may include an application-specific integrated circuit (ASIC).

The various embodiments of the disclosure may be implemented as softwareincluding instructions stored in machine-readable storage media, whichcan be read by machines (e.g.: computers). The machines may refer, forexample, to apparatuses that call instructions stored in a storagemedium, and can operate according to the called instructions, and theapparatuses may include an electronic apparatus according to theaforementioned embodiments (e.g.: an electronic apparatus 100). Where aninstruction is executed by a processor, the processor may perform afunction corresponding to the instruction by itself, or using othercomponents under its control. An instruction may include a code made bya compiler or a code executable by an interpreter. A storage medium thatis readable by machines may be provided in the form of a non-transitorystorage medium. The ‘non-transitory’ storage medium may not includesignals, and is tangible, but does not indicate whether data is storedin the storage medium semi-permanently or temporarily.

According to an embodiment of the disclosure, methods according to thevarious embodiments described in the disclosure may be provided whilebeing included in a computer program product. A computer program productrefers to a product, and it can be traded between a seller and a buyer.A computer program product can be distributed on-line in the form of astorage medium that is readable by machines (e.g.: a compact disc readonly memory (CD-ROM)), or through an application store (e.g.: PlayStore™). In the case of on-line distribution, at least a portion of acomputer program product may be stored in a storage medium such as theserver of the manufacturer, the server of the application store, and thememory of the relay server at least temporarily, or may be generatedtemporarily.

Further, each of the components according to the aforementioned variousembodiments (e.g.: a module or a program) may include a singular objector a plurality of objects. Among the aforementioned corresponding subcomponents, some sub components may be omitted, or other sub componentsmay be further included in the various embodiments. Generally oradditionally, some components (e.g.: a module or a program) may beintegrated as an object, and perform the functions that were performedby each of the components before integration identically or in a similarmanner Operations performed by a module, a program, or other componentsaccording to the various embodiments may be executed sequentially, inparallel, repetitively, or heuristically. Or, at least some of theoperations may be executed in a different order or omitted, or otheroperations may be added.

While various example embodiments have been illustrated and described,it will be understood that the various example embodiments are intendedto be illustrative, not limiting. Accordingly, one of ordinary skill inthe art will understand that various changes in form and detail may bemade without departing from the true spirit and full scope of thedisclosure, which includes, for example, the appended claims and theirequivalents.

What is claimed is:
 1. A method of controlling an electronic apparatus,the method comprising: receiving input of a first utterance; identifyinga first task for the first utterance based on the first utterance;providing a response to the first task based on a predetermined responsepattern set for the first task; receiving input of a second utteranceafter the response to the first task is provided; identifying a secondtask for the second utterance based on the second utterance; providing aresponse to the second task based at least on a predetermined responsepattern set for the second task; determining a degree of associationbetween the first task and the second task; and setting a responsepattern for the first task based at least on the determined degree ofassociation between the first task and the second task satisfying apredetermined condition.
 2. The method of claim 1, wherein the responsepattern is determined while including at least one of informationrelated to a length of a response to an utterance or information relatedto types of information included in the response to the utterance. 3.The method of claim 1, wherein the predetermined response pattern setfor the first task includes a response pattern selected by a command, oran automatically set response pattern.
 4. The method of claim 3, furthercomprising: inputting a voice based on the first utterance into atrained artificial intelligence model to obtain information on anacoustic feature of the first utterance; and recognizing a user based onthe information on the obtained acoustic feature, wherein thepredetermined response pattern set for the first task is determinedbased on a recognized conversation history and preference information ofthe user.
 5. The method of claim 1, wherein the predetermined conditionincludes a condition wherein information on the degree of associationbetween the first task and the second task is equal to or greater than athreshold value.
 6. The method of claim 1, further comprising:determining whether a third task for a third utterance is associatedwith the first task based on receiving input of the third utterance; andproviding a response based on the predetermined set response pattern forthe first task.
 7. The method of claim 1, further comprising: receivinginput of a third utterance; and identifying a third task for the thirdutterance based on the third utterance, and determining a degree ofassociation between the first task and the third task, and wherein thesetting further comprises: determining priorities of the second task andthe third task based on the determined degree of association between thefirst task and the third task satisfying a predetermined condition; andsetting a response pattern for the first task based on the determinedpriorities, the second task, and the third task.
 8. The method of claim5, further comprising: acquiring feedback for the provided response; andupdating the degree of association between the first task and the secondtask based on the acquired feedback.
 9. The method of claim 1, furthercomprising: storing the first task and the second task in a form ofontology based on the degree of association between the first task andthe second task.
 10. The method of claim 1, wherein at least one of theidentifying a first task, the determining the degree of association, thesetting a response pattern, or the providing a response is performed byan artificial intelligence model, and the artificial intelligence modelincludes a plurality of layers respectively including at least one node,and each of the at least one node includes a neural network model havinga connection weight for interpretation of an input value.
 11. Anelectronic apparatus comprising: a memory configured to store at leastone command; and a processor, comprising circuitry, coupled to thememory and configured to execute the at least one command, wherein theprocessor is configured to control the electronic apparatus to: receiveinput of a first utterance, identify a first task for the firstutterance based on the first utterance, provide a response to the firsttask based on a predetermined response pattern set for the first task,receive input of a second utterance after the response to the first taskis provided, identify a second task for the second utterance based onthe second utterance, provide a response to the second task based on apredetermined response pattern set for the second task, determine adegree of association between the first task and the second task, andset a response pattern for the first task based on the second task basedat least on the determined degree of association between the first taskand the second task satisfying a predetermined condition.
 12. Theelectronic apparatus of claim 11, wherein the response pattern isdetermined while including at least one of information related to alength of a response to an utterance or information related to types ofinformation included in the response to the utterance.
 13. Theelectronic apparatus of claim 11, wherein the predetermined responsepattern set for the first task includes a response pattern selected by acommand, or an automatically set response pattern.
 14. The electronicapparatus of claim 13, wherein the processor is configured to controlthe electronic apparatus to: input a voice based on the first utteranceinto a trained artificial intelligence model to obtain information on anacoustic feature of the first utterance, and recognize a user based onthe information on the obtained acoustic feature, and wherein thepredetermined response pattern set for the first task is determinedbased on a recognized conversation history and preference information ofthe user.
 15. The electronic apparatus of claim 11, wherein thepredetermined condition includes a condition wherein information on thedegree of association between the first task and the second task isequal to or greater than a threshold value.
 16. The electronic apparatusof claim 11, wherein the processor is configured to control theelectronic apparatus to: determine whether a third task for a thirdutterance is associated with the first task based on receiving input ofthe third utterance, and provide a response based on the predeterminedset response pattern for the first task.
 17. The electronic apparatus ofclaim 11, wherein the processor is configured to control the electronicapparatus to: receive input of a third utterance, identify a third taskfor the third utterance based on the third utterance, and determine thedegree of association between the first task and the third task, anddetermine priorities of the second task and the third task based on thedetermined degree of association between the first task and the thirdtask satisfying a predetermined condition, and set a response patternfor the first task based on the determined priorities, the second task,and the third task.
 18. The electronic apparatus of claim 16, whereinthe processor is configured to control the electronic apparatus to:acquire feedback for the provided response, and update the degree ofassociation between the first task and the second task based on theacquired feedback.
 19. The electronic apparatus of claim 11, wherein theprocessor is configured to control the electronic apparatus to: storethe first task and the second task in a form of ontology in the memorybased on the degree of association between the first task and the secondtask.
 20. The electronic apparatus of claim 11, wherein at least one ofthe identifying a first task, the determining the degree of association,the setting a response pattern, or the providing a response is performedby an artificial intelligence model, and the artificial intelligencemodel includes a plurality of layers respectively including at least onenode, and each of the at least one node including a neural network modelhaving a connection weight for interpretation of an input value.